Streaming data can be used to represent a wide variety of phenomena such as the price of a company stock over time, a rate of fluid flow through a pipe or a physical process within a human body. Streaming data may exhibit distinct patterns of behavior that may be detected, for example, by analyzing dynamical or statistical properties of the data. Streaming data with similar patterns of behavior may be categorized as being within a single regime.
The ability to categorize streaming data into regimes and to detect a change in the regime of streaming data can provide useful information, such as signaling an anomaly or abnormality in underlying phenomena represented by the streaming data. For example, a regime change in streaming data derived from stock price may provide a buy or a sell signal in a technical analysis system. A regime change in streaming data derived from rate of fluid flow through a pipe may indicate a significant event in a well that is the source of the fluid. A regime change in streaming data derived from an electrocardiogram may signal a significant cardiac event.